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Ten simple rules for a successful international consortium in big data omics

1 CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain, 2 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain, 3 Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain, 4 Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain, 5 Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain, 6 Universitat Pompeu Fabra (UPF), Barcelona, Spain

a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 (https://www.icgc-argo.org/news/8/icgc-tcga-pan-cancer). We have summarised our experience into 10 rules as a guideline for future consortia dealing with large dataset and analyses, particularly when operating without central funding. Although these 10 rules should be obvious and may seem rather straightforward, most, if not all, are nevertheless often broken, ignored, or forgotten.

Rule 1: Set up a transparent and effective governance
For a consortium to be successful, it needs to have a transparent and effective governance. The 2 key components of this are a clear set of rules and a well-defined organisational structure. The importance of a transparent set of rules, adhered to by all consortium members, is frequently overlooked. The rules should detail the organisational structure, what is expected from each participant and the foreseen outcomes of the consortium (Rule 2), with special emphasis in the publication and communication policy (Rule 9). For example, there should be clear rules on authorship. How will the long list of authors be shown? Who will be listed as named authors in the main paper? What are the rules to include authors in the companion papers? Another important part of the set of rules should be how to deal with foreseeable conflicts of interest. All these rules should be agreed upon before the actual start of the project. In this way, people can decide whether they would be willing and able to adhere to the rules defined. In centrally funded projects, this often adopts the form of a consortium agreement as part of the grant application. Grant providers like the European Commission provide documents with guidance on this (https://ec. europa.eu/research/participants/data/ref/h2020/other/gm/h2020-guide-cons-a_en.pdf). We should not forget a consortium agreement is mandatory for good reason, and even without central funding, there still should be something similar to it. Importantly, there should be no further changes to the rules once the project has started, unless major unforeseen circumstances warrant this. In which case, the majority of the consortium should support the change and thereby avoiding that important decisions that affect all are being made by just a few people.
For the second component of the governance of the consortium, the organisational structure, the division of the consortium into smaller working groups, especially in big consortia, should form the foundation. On top of that structure, there should be a steering committee. The working groups serve to organise scientific discussion around specific topics, do the analyses, and write the publications. The main tasks of the steering committee should be to enforce the rules in the day-to-day functioning of the consortium, ensure that the work within the consortium contributes to the overall scientific objectives (Rule 2), and is in line with its quality standards. The committee must guarantee the functioning of working groups and coordination among them. This is essential for the flow of ideas between scientists within the consortium, enhancing creativity, which is key to further science. They should make sure that the consortium achieves its goals in a timely manner (Rule 8). The steering committee should also provide a unified representation of the consortium in front of stakeholders, in particular scientific journals and the media, and defend the interests of the entire consortium. If the steering committee members are also scientific players in the consortium, this poses potential conflicts of interest. Although this is not ideal, in practice this may need to be the case as otherwise committee members do not get much in return for their effort and it is hard to follow all the insand-out of a project from progress meetings. The rules agreed upon should detail a way to deal with this double role of the steering committee.

Rule 2: Know where you are going by setting clearly defined goals
The goals of the consortium need to be clearly defined, as failure to do so will have a ripple effect throughout many facets of the project. Clear goals are needed to be able to define and agree on the right terminology (Rule 3), to have proper time management (Rule 8), to define a publication strategy (Rule 9), and to ensure others can build upon the results obtained (Rule 10). Aligning with the goals of the project already starts at the data acquisition stage. Despite that it may be tempting to cast a wide net and collect as much data as you can get your hands on, irrespective of relevance, available metadata and quality, there are risks involved in this "the more, the better" strategy. Some of the data collected may not be relevant to answer the questions at hand or insufficient in quantity to answer questions nobody initially thought of. For example, if the main objective is to study primary tumours, including a handful of metastatic tumours is unlikely to be of any use. If irrelevant data is included, the main signal of interest of the project could be distorted or the interpretation of the results complicated. It could also lead to wasting valuable time and resources on samples that cannot be used later on. For similar reasons, it is also important to specify from the beginning the minimum required quality of samples for inclusion in the study, and what metadata is essential, the absence of which would lead to the exclusion of samples later.

Rule 3: Use the right tools and terminology to communicate
Communication is essential to enable collaboration, exchange ideas, inspire others, and ensure everyone is on the same page. In projects involving hundreds of researchers from various backgrounds, efficient communication is challenging. It requires the use of the right communication tools and the development of a shared "language." There are a multitude of tools available to facilitate collaborations among extended groups, some of which increased in popularity during the COVID pandemic. It is key, however, to choose the ones best suited for the type of information that needs to be conveyed (Fig 1). One also has to remain practical in choosing Sending an e-mail is not always the right solution and could result in an avalanche of messages. It is, however, also not necessary to have a tele-/videoconference for every little thing. This flowchart proposes a way to decide which tool is likely to be the most appropriate for the given purpose. Key is to keep in mind how much interaction is needed between the people involved and how large this group of people is. Generally, a videoconference is preferred over a teleconference, especially when the group of people involved is large. For some of the more specific points of communication like mistakes in data or software, there are specialised tools available. F2F: face-to-face. 1 Synapse: www.synapse.org. 2 JIRA: https://www.atlassian.com/software/jira. which resources to use to facilitate collaboration. These resources must be easily accessible, for example, avoiding the hassle of having to log onto several different systems and having to make yet another account. A few simple rules should be respected to guarantee the success of tele-/videoconferences (Fig 2). We would also suggest recording online conferences to share them across the consortium. Furthermore, a balance must be attained between the need for (scientific) discussions and the time this takes away from other tasks.
The challenge and importance of defining a shared "language," i.e., identifiers and terminology, should not be underestimated. It is important to ensure everyone interprets terms in the same way and decisions in this regard should be made at the beginning of the project or when the term in question becomes relevant. Existing ontologies like those that can be found in the OBO Foundry (https://obofoundry.org/) could aid in this. Some leeway may need to be given for changes in terminology to accommodate new insights. However, one should keep in mind that once a certain identifier or term is introduced in a consortium of hundreds of people, it becomes next to impossible to change it. It acquires a life of its own, is used in programming code, manuscripts, figures, and so on. Trying to change things midway may result in multiple identifiers and terms being used next to each other, which in turn leads to confusion. At the publication stage, it is impossible to control that all manuscripts from the consortium adhere to the same terminology and identifier, if they have not been defined and agreed upon beforehand. Finally, as articles of large consortia are likely to turn into a reference by research projects to follow, ensuring the use of correct terminology and if necessary resolving any discordance in the field should be seen as the responsibility of the consortium and part of its legacy.

Rule 4: Keep in mind you are collaborators, not competitors
In particular for very large consortia splitting into working groups makes communication easier within the groups and speeds up their work. However, there will always be a degree of overlap between working groups and results from one group can be important for other groups. Ultimately, all groups work on the same data. Consortium members should share all of their progress instead of keeping their cards close to their chest. It is important not to lose sight of the fact that within a consortium, you are all collaborators rather than competitors. Sharing the abstracts of proposed manuscripts with the entire consortium as early as possible is a good idea to ensure there are no duplicate efforts and potential collaborations could be discovered. One note of care: Although there is nothing wrong with getting inspired by the work of others, credit should be given where credit is due and mindfulness is important to not give the impression ideas were stolen.

Rule 5: Build in checkpoints at every stage of the project
As the famous principle in computer science goes, "Garbage in, Garbage out." This holds true for every stage of a project, from the primary data down to, for example, mutation calls. Examples of what to check at the various stages are listed in Table 1. It is key to detect any issues with the input at every stage in terms of quality and potential errors as early as possible. It is also important to be aware of missing data and record why it is missing. Not detecting any structural mutations in a tumour sample is not the same as a pipeline unable to call structural mutations for this specific sample due to technical issues. In case the missing values can be imputed and there is a desire to do so, you need to know whether or not they are missing at random [6]. By detecting issues early, you can prevent resources from being wasted on analysing samples that do not meet quality standards and prevent errors from propagating to downstream analyses. Having to rerun analyses is not only demotivating, it is also time and resource consuming. Moreover, rerunning may be a luxury a consortium cannot afford, forcing it to adopt an imperfect solution instead. Working with ever-changing input data is frustrating for researchers involved in downstream analyses. In addition, having multiple versions of a certain (derived) dataset floating around may lead to different groups using disparate versions. Definite data freezes are very important. Samples that in hindsight should have been excluded, must not be kept for the sole reason that resources have already been spent on them. It is important to know when to cut your losses. Low-quality samples will introduce noise, while adding very little-if any-useful information to the analysis. In a project where data is collected from different sources that use different protocols, quality is likely to vary, which is important to be aware of and to quantify [7]. This allows researchers to potentially correct for batch effects and be confident that their conclusions are not just reflecting differences in quality between samples or are based on lower quality samples.
Finally, before data or a tool is shared with the entire consortium, the responsible working group has to ensure there are no mistakes in it. However, it is not easy to spot your own mistakes. Using the entire consortium as a testing ground may increase the chances of mistakes being identified, but it is also a costly strategy. An initial check by a selected test team, not involved in the analysis in question, can help prevent many errors from burdening the project. This would be similar to the dedicated test teams in commercial companies.

Rule 6: Learn how to walk before you run
In a "wet-lab" setting, you would never use all your starting material at once without going through several testing phases on small amounts of material to ensure every step of the process works. In bioinformatics analyses, however, there is the tendency to run pipelines immediately on the entire dataset. For the primary analyses, like alignment and mutation calling, the goal is to allow analyses downstream to start as soon as possible. As no physical material is being wasted, it seems this can be done with impunity, but in reality it might result in wasting (computational) resources. Slow and steady is more likely to win the race. The effort to apply someone else's software tool, even a published one, should not be underestimated. The developer of a tool is often the most adept user. Software is often developed and tested in a specific computer environment. Moreover, there are multiple settings in a tool that have to be tuned for the dataset at hand.
Running the workflow initially on a subset of the data also makes it easier to detect if the chosen terminology is adequate (Rule 3), to detect inconsistencies, and to ensure no errors have been made (Rule 5). This is especially important for the primary analyses as they form the foundation for all subsequent analyses and having to redo this part will have far-reaching consequences. The initial test set should be as representative as possible of the entire set of samples and once it runs smoothly without apparent errors, a first release can be shared with the consortium for downstream analyses. From here on, one can start to run the workflow on an increasingly larger dataset with intermediate, incremental releases to the consortium. At this point the workflows, tools, and terminology used should be frozen, as continuous changes to these things make it impossible to continue with the next step. Note that some errors may only come to light with particular samples, which is why a test team is essential (Rule 5). If multiple tools are applied to the same data and subsequently a consensus is built, then it is important to run the samples in the same order through the pipelines.

Rule 7: Keep track of code and data provenance
For the purpose of data provenance and as part of adhering to the Findable, Accessible, Interoperable and Reusable (FAIR) principles [12], what should be stored ranges from which institute sequenced a sample to the "in silico" processing steps. This is even more important when the data comes from many different sources each with their own system in place. This may lead to, among other things, a multitude of identifiers for the same sample. The facility that collects the sample attaches an identifier, so does the sequencing facility and if submitted to a public repository another identifier is linked to it to make the sample unique in a cross-project platform. Another one might even be added to give the sample a universally unique identifier (UUID) [13], which would be the preferred one to use, although due to its length it is impractical for use in article figures. Purely for the use in figures, the identifier assigned by the (public) repository could serve as an alternative to the lengthy UUID. Each identifier linked to a sample at any stage should be recorded and shown in the repository for traceability. In the case of multi-omic data, it is also essential to ensure the identifiers accurately reflect whether the different types of omics were derived from the exact same sample or only the same donor.
Recording data provenance makes it possible to trace back the origin of anomalies and quality issues detected further down the process. For example, the use of PCR amplification in the sample preparation increases specific artefacts [8,9]. Having the use of PCR amplification recorded is better than having to discover this post hoc. Knowing which analyses were done, the settings used and filters applied are all important as it allows everyone to understand how results need to be interpreted and the potential limitations of the data. It also makes it easier for others to reproduce results and build upon them (Rule 10). There are various tools available to keep track of software that is being developed and facilitate a clear description of the workflow. Examples are Git for version control (https://git-scm.com/) and the virtual environment management system Conda (https://docs.conda.io/en/latest/). Workflow managers like Jip (https://pyjip.readthedocs.io/en/latest/index.html), Nextflow [14], and Snakemake [15] can provide a way to describe the steps and share with others. Also advisable is sharing files containing data and intermediate results across working groups, using platforms such as Synapse [16], capable of tracking file provenance and versioning. In summary, wise use of existing tools may go a long way to guarantee data provenance and software availability.

Rule 8: Keep up the pace and finish within a reasonable timeframe
Time management is especially complicated in projects without central funding, as there is no overall accountability and no mandatory reporting in place to put pressure on people to deliver their contribution to the project. Also, the availability of resources depends entirely on what participating groups can offer and it is difficult to estimate the time people will be able to invest. The time needed for the "blue collar" part of a project is often underestimated, for example, the logistics behind sharing omics data and the potentially associated legal issues. For large datasets, sufficient bandwidth to transfer the large volumes of data needs to be taken into account. As advocated by Stein and colleagues [17], data should be available in a more accessible place like a cloud. Taking the computation to the data avoids transferring data. However, this might come at the cost of having to invest time to make a tool work in a different compute environment than in which it was developed. Although this process is often more time-consuming than expected (Rule 6), it is worthwhile for large datasets, especially given the added legal issues of moving data out of the place where it was generated. Producing and distributing software that can seamlessly be run across platforms is also a very important responsibility of large consortia (Rule 10).
There may not be a set end date of the entire project aside from the data embargo that may need to be lifted at some point. This can be problematic, especially for PhD students and postdoctoral fellows, as they often have short-term contracts and are under time constraints to publish their work. Delays in publishing the main consortium article might hold back companion articles. An effective mitigation strategy is to go for a more flexible publication policy, where you do not wait for the main consortium paper to be accepted first (Rule 9). It is also important to consider that the novelty of the project's findings decreases with time. Delays may thus diminish the impact of the results.
Even without externally imposed deliverables by funding agencies, deadlines have to be set and met. It is important to establish a realistic timeline and abandon elements that hold back the group if they are not essential for the project. Laying down overly aggressive timelines and then letting them slip, reduces the enthusiasm of the group and leads to frustration. Consortium members must refrain from overpromising results, and must spend the time on the project as promised, and complete the tasks assigned to them (Rule 1). Even if there is no funding party imposing penalties for not sticking to what was written in the original proposals, there needs to be discipline among the consortium members.

Rule 9: Define an effective and fair publication strategy
The basic principles behind an effective and fair publication strategy are simple: ensure the science is communicated clearly and that credit is given where credit is due. Failure to meet the expectations of consortium members in terms of publications is demotivating and leads to frustration. Proper credit should be given not only to those with tenure, but also especially to those without it, i.e., PhD students and postdocs. The mere existence of the aphorism "publish or perish" is a testament to the importance of publications. The number of articles, the journals where they are published, the length of the author list, and the position each person has in it are all important for the evaluation of scientific performance. While this system may be flawed in many aspects, it is the one in place to communicate scientific results. One could rightly argue that large consortia could have the power to bring around important changes in the system, including setting the requirements that the main consortium article should satisfy.
The shape and content of this main article may be a particular point of contention between a consortium and a journal. It is in the interest of journals to propose to put all or most of the results in a single publication that attracts many readers and citations, thus increasing their impact factor. However, cannibalizing companion articles is detrimental to the principle of clarity. It is hard for the authors to explain their stories in a clear way in a long article with many and diverse results intermingled. These dense and long articles are also very difficult to digest by the reader. A lot of relevant information is relegated to the supplementary section, which steals the thunder of companion articles. The main consortium article also enters into conflict with the principle of giving proper credit to authors, as a long list of authors under a consortium name, with few highlighted as named authors or corresponding authors, does little justice to the time and effort put in by PhD students and postdocs. The particle physics community lists authors in alphabetical order and at least in this way provides equity.
One solution to this problem is that the main consortium article provides a global perspective on the project, the datasets generated, the technical challenges faced and their solutions, while the scientific discoveries are left to companion articles, which the main article cites. The companion articles with fewer authors and a focused story with a clear message are likely to be of more value to the scientific community (writers and readers). Such a strategy would also free companion articles for publication as they become ready, rather than forcing them to wait for the publication of the main consortium article. The main article would still be highly cited, as researchers would refer to it as the source of the data and when citing the main achievements of the consortium. An early, skeleton version of the main article could serve as a point of reference within the consortium to ensure all basic information, like the number of samples, and terminology is the same across all companion papers.
There is little use to start negotiations with one specific journal before there is a clear idea of the outcome of the project. This just increases the chance of creating unrealistic expectations for both parties. Instead throughout the project it should be evaluated what the best strategy will be with the agreement of consortium members. Too close collaboration between a journal and a consortium jeopardizes the fairness of the peer review process as the willingness of the journal to accept a manuscript will be higher than normal.
Another recommendation is to publish the results as preprints as soon as they are ready to be shared with the scientific community, which helps speed up the scientific process. As a preprint can already be cited, even at this stage it is important to give credit to all involved and to include the contributions of each person, which is in any case a requirement of many journals. This also prevents some people from being authors on all articles coming out of the consortium, while others appear on less than they should. This does require everyone to be realistic about their contribution.
Finally, a great resource to provide valuable feedback and further improve the quality of a manuscript before submitting it, are those consortium members that were not involved in the particular manuscript. A system should be in place to ensure that the work that comes with this is equally distributed across the consortium members and that for each manuscript the members with the right expertise are asked to provide feedback. What should be avoided, however, is that, for example, the steering committee takes the role of reviewer, preventing manuscripts from being submitted and thereby interfering with the normal peer-review process. It also puts all the power regarding the publication strategy in the hands of just a few people (Rule 1) and can cause delays, compromising the timely publication of results (Rule 8). Moreover, if the members of the steering committee are also involved in multiple subprojects, this is a source of a potential conflict of interests or at the least appearance of it. In this regard, the journals are still the ones responsible for selecting unbiased and sufficiently experienced reviewers, and although the pool of possible reviewers may be depleted in the case of large consortia, there will always be qualified external reviewers able to do the job.

Rule 10: Be the giant on whose shoulders others can stand
It is key that future projects can build on the results produced by the consortium. Even before starting your project, you should think about what the next consortium might need to be able to continue where you left off (Rule 2). For example, if the project involves sequencing data, it is worth doing sequence alignment to the most recent version of the genome reference even if it at the moment is not the one most commonly used. If this is not done, it could mean the data becomes obsolete faster. Or if the next project wants to use it, a lot of work might have to be put in to bring the data up to date. Alternatively, a new project may choose to use the data as is to avoid this, which basically sets back the field as it is not using the improved version of the reference genome.
Sharing the results in the form of articles is only one way for a consortium to leave a legacy. As detailed in the 10 simple rules of Boland and colleagues [18], it is also essential to share data and tools such that others can build on it. A common phenomenon in bioinformatics is that tools mainly seem to work for the people who developed them. Others trying to use them often encounter difficulties and need to invest significant time to get them to run. This is why it is so important to document all the tools used, make them available in a format that everyone can use and when applied to the same data will get the same answers. To ensure that the workflow is portable between different operating systems, container technologies such as Docker (https://www.docker.com/) or Apptainer (https://apptainer.org/) may be used. Code can be documented and shared using among others, Jupyter notebooks (https://jupyter.org/index. html), Rmarkdown [19], and open source repositories like Code Ocean [20] and Zenodo (https://zenodo.org/). Optimising the tools in terms of speed and memory usage is also worth considering given that datasets will continue to grow and thereby the return on the investment increases. It will be a valuable legacy for future endeavours.
Aside from the software, it is also important to share the data along with the associated metadata and data provenance (Rule 7). This allows the next project to build on top of it and take the field to the next level. All of this taken together can in short be summarised as that you need to make sure you adhere to the FAIR principles [12].

Concluding remarks
Key for a successful consortium is to ensure you indeed go farther and not just slower. There is a real risk that in failing to keep up the pace, a large-scale consortium can hold back the field rather than stimulate progress. A consortium should be a platform for the development and promotion of new ideas. The data and findings should also not be held hostage for the glory of a few. Making an effort to stay friends is of great importance as well. Building consortia to tackle big research questions is key to advance science. We hope that these 10 simple rules may help future consortia to indeed go farther and build networks that are enriching, particularly for young scientists.


Identification of Nine mRNA Signatures for Sepsis Using Random Forest

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.

Introduction
As a clinical syndrome, sepsis has been accompanied by human society from ancient times to the present [1]. Sepsis and septic shock have high fatality rates and consume a large amount of medical resources. Since the launch of save sepsis in the early 2000s, the treatment outcomes of patients with sepsis have improved. But the case fatality rate for sepsis remains at 25 to 30 percent, and when shock occurs, it can be as high as 40 to 50 percent [2]. After decades of research, there is still no specific treatment for sepsis. The improvement in patient outcomes came primarily from nonspecific interventions, including fluid resuscitation, early application of antibiotics, and elimination of the source of infection ( [3] #5; [4] #8478; [5] #8582; [6] #49). An important reason for this disheartening situation is that the definitions of sepsis and septic shock cover a very heterogeneous population of patients. The causes are so varied that it is difficult to find a common treatment for these conditions.
How to classify patients with sepsis is one of the key areas of research on sepsis and other diseases [7][8][9], though biomarkers have been the subject of intensive research for decades ([10] #71; [11] #15; [12] #8853; [13] #431; [14] #673; [15] #50). For example, procalcitonin has been included in treatment guidelines [16], but there is currently no reliable biomarker to distinguish between infected and uninfected patients. Only 30-40% of patients with sepsis or septic shock have positive blood cultures. New technologies such as high-throughput technologies (genomics, transcriptomics, etc.) have been used to better identify subsets of patients with sepsis, to identify patients at high risk of developing sepsis, and to provide the possibility for rapid and accurate diagnosis of infection [17,18,19].
This study analyzed microarray dataset from public gene expression database, to obtain differentially expressed genes (DEGs) between sepsis and healthy people, and then, a random forest model was performed on the DEGs to select more import biomarkers. Next, we performed gene functional enrichment analysis on the DEGs selected to analyze the function module of the DEGs and to uncover how the DEGs contribute to sepsis. Our study aims to detect neglected biomarkers of sepsis to better distinguish between sepsis patients and healthy controls.

Data Resource.
To identify potential gene signatures associated with sepsis, we got two gene expression datasets (GSE154918 and GSE131761) [20,21] from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), GSE154918 dataset as primary research data and GES131761 as supplementary data.
Totally, 109 samples from GSE154918 dataset were collected from 19 septic shock patients, 20 sepsis patients, 12 uncomplicated infection patients, and 40 healthy volunteers. Supplementary validation dataset GSE131761 was collected from 81 septic shock patients and 15 healthy volunteers. All samples were collected from peripheral blood. The diagnosis of septic shock was according to the Sepsis 3.0 criteria [3].

Identification of DEGs.
The workflow is shown in Figure 1. DEGs were calculated between sepsis samples (uncomplicated infection, sepsis, and septic shock) and healthy control using Limma package [22] with p value < 0.05 and |logFC | >1 as threshold.

Random Forest
Model. Random forest model was performed using Python machine learning library Scikit-learn [23], with expression matrix of all genes as feature and disease state as label as other researches [1]. We set 1000 random forest trees and operated 5-fold cross-validation and leave-one-out cross-validation to evaluate the performance of the model. Feature importance was collected from the random forest model after training and assessment, and then, we sorted the features by feature importance and chose top n features to reconstruct random forest, accessing the best combination of gene signatures [24].

Functional and Pathway Enrichment Analyses.
Gene enrichment analysis of DEGs was based on Gene Ontology (GO) database from molecular function, cellular component, and biological process using R package ClusterProfiler [25]; pathways with adjusted p value < 0.05 were selected as significant enriched pathways [26].

Results
3.1. Identification of DEGs. Gene expression difference was calculated between three sepsis groups and healthy control, respectively. 530 differentially expressed genes (DEGs) were found in uncomplicated infection patients compared with healthy control, 727 DEGs were found in sepsis samples, 1414 DEGs were found in sepsis shock samples, and 384 common DEGs were found in the above three contrast groups ( Figure 2). We found that as the condition worsens, more genes were under disorder condition.
3.2. Features Selected by Random Forest. Next, we performed random forest [15,27] to select important genes of the DEGs of the above three contrast groups, with gene expression matrix of DEGs as feature and health state as label; we selected the genes with feature importance > 0 as the most important genes. We, respectively, found 440, 657, and 1018 DEGs in uncomplicated infection vs. healthy control, sepsis vs. healthy control, and sepsis shock vs. healthy control, and 279 genes were common among the three ( Figure 3).

Enrichment Analysis of Intersection Important DEGs.
We further performed functional analyses for 279 important DEGs to explore the underlying biological roles. Multiple GO-BP terms were associated with neutrophil degranulation, neutrophil activation involved in immune response, and neutrophil-mediated immunity. The DEGs played essential roles in GO-MF terms containing 2 more enriched terms: RAGE receptor binding and long-chain fatty acid binding. The GO-CC revealed that these DEGs were mainly enriched in specific granule, tertiary granule, and secretory granule lumen ( Figure 4).

Biomarkers Distinguishing Disease States.
To detect biomarkers to distinguish three disease-state patients and healthy patients, we further performed random forest on three disease states and healthy samples together, sorted the feature importance, and selected the top n (1-50) gene features to reconstruct random forest model to access the best biomarker combination. We found that when 6 features were selected, the accuracy of the model reached 0.895, and the accuracy of model began to decline since more than 9 features were selected. Therefore, the first 9 characteristics were selected as potential biomarkers to predict different sepsis states ( Figure 5).

Supplement Validation.
In order to assess the availability of the 9 biomarkers, we used a supplementary validation dataset to build random forest model using these 9 biomarkers as feature. Since the biomarker MCEMP1 was not sequenced because of lacking probe, only 8 biomarkers were sequenced in supplement dataset, so we only evaluated the 8 biomarkers. We found that the model performed good to distinguish sepsis from healthy control in supplement dataset. Then, we further calculated the gene expression difference between sepsis and healthy control samples in supplement dataset, and we found that 6 out of 8 biomarkers were DEGs in supplement dataset (Table 1).

Discussion
According to the Sepsis 3.0 definition, sepsis is a lifethreatening organ dysfunction resulting from an infectioninduced host response disorder. Neutrophils are the main immune-cell barrier against pathogens, but they can be a double-edged sword in sepsis because they play a role in both proinflammatory response and anti-inflammatory response. We hypothesize that the immune signature of sepsis can be determined early by the phenotype of neutrophils and distinguish sepsis from noninfectious inflammatory syndromes. It is important to screen for features that are considered important in the biology of sepsis but alone are not distinguishable to clearly distinguish sepsis. Sepsis is thought to be an immune imbalance in which pathogens evade the host's defense mechanisms and continue to stimulate and destroy host cells. Many of the protective immune mechanisms activated early in the disease become harmful and are associated with excessive inflammation and immunosuppression. The host response of sepsis involves the coexistence of inflammatory and anti-inflammatory  3 Computational and Mathematical Methods in Medicine responses, involving different organs, systems, and cell types [28].
We conducted a differential analysis of data from a group of three sepsis severity levels and healthy controls and found that the number of differential genes in sepsis patients increased according to the severity of the disease, suggesting that more genes became dysregulated with the severity of the disease. There were 279 differentially expressed genes in all three kinds of severe infection, which may play roles in the onset and progression of sepsis. Functional enrichment analysis showed that biological processes were most significantly enriched in neutrophil activation, immune activation, inflammatory response, and bacterial response.
In GO-MF analysis, DEGs were significantly enriched in RAGE receptor binding and long-chain fatty acid binding. A meta-analysis showed that RAGE inhibition had a significant advantage in multiple microbial infections. For G + bacterial infection, RAGE suppression reduced bacterial growth and transmission, inflammatory cell flow, plasma cytokine levels, and lung damage. This paper concluded that RAGE inhibition had beneficial effects on the outcomes of animal models of sepsis with different causes [29]. There are few studies on long-chain fatty acid binding and sepsis. This article is one of them. Extraenteral pathogenic E. coli can cause diseases such as urinary tract infections and sepsis. Mucus is the main nutrient source of Escherichia coli in the intestinal tract, and genes directly or indirectly related to the fatty acid oxidation pathway contribute to the adaptation and migration of ExPEC [30].
In our study, the remarkable GO-CC terms are associated with neutrophil degranulation, such as tertiary granule, specific granule, and secretory granule lumen. Neutrophils are one of the most important cells in the host's natural defense. The following are the granules in neutrophil cytoplasm: azurophilic granule, specific granules, gelatinase granules, and secretory vesicles. They all play very important roles. Neutrophil dysregulation is present in sepsis. Many evidence suggest that neutrophil threshing molecules are of value in the diagnosis and prognosis of sepsis. Monitoring neutrophil function may help identify early sepsis [31].
We used the random forest to select 9 characteristic genes as potential biomarkers for predicting sepsis: MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A. Some of these genes have been confirmed in experiments or have also been widely concerned in bioinformatics studies.
MCEMP1 is involved in the regulation of mast cell differentiation or innate immune response. In our study, MCEMP1 gene expression was increased in sepsis. Chen et al. [32]. established a cecal ligation and puncture-induced sepsis mouse model to determine the expression of mast cell expression membrane protein 1 (MCEMP1). They observed that MCEMP1 was highly expressed in septic mice. Loss of MCEMP1 can promote T lymphocyte and NK cell activity, increase immunoglobulin expression, inhibit the release of inflammatory factors, and reduce T lymphocyte apoptosis. They also found that downregulation of lncRNA NEAT1 could inhibit MCEMP1, thereby promoting the immunosuppression effect of Mir-125 on sepsis mice. This may be a potential therapeutic target for sepsis. Xie et al. found that MALAT1 upregulates MCEMP1 by binding to Mir-23a, thereby promoting inflammatory response in sepsis mice [33].
Proline-serine-threonine-phosphatase-interacting protein 2 (PSTPIP2) belongs to the F-BAR family of proteins and is mainly expressed in macrophages. In recent years, PSTPIP2 has been found to play an important role in congenital immune diseases and acquired immune diseases (AIDS) [34]. Chen et al. [35] studied biomarkers of Escherichia coli-induced sepsis. They analyzed 4 microarray datasets from GEO database and identified 54 DEGs. Eight different genes were found between sepsis patients and controls. Furthermore, differential expression of the candidate gene was verified by human blood model in vitro. qPCR results suggested that PSTPIP2 may be closely related to Escherichia coli-induced sepsis.
Neutrophils play an important role in the pathophysiology of sepsis and are the primary defense against infection. A transcriptome study was performed on purified neutrophils from patients with septic shock to identify genes that were differentially expressed during the first week of illness compared with healthy controls. The results were confirmed at the protein level. They found that 364 differentially expressed genes were upregulated and 328 downregulated in patients with sepsis. CD177mRNA showed the most significant difference between patients and healthy controls. This is consistent with our findings, which also found that CD177 was significantly upregulated in sepsis patients [36].
Yang and Li [37] applied bioinformatics to study the molecular mechanism of sepsis. Transcriptome data (GSE12624) were downloaded from Gene Expression Omnibus database for protein-protein interaction network analysis. Twenty-four differentially expressed clusters were identified by ANCOVA global test, including 12 clusters in sepsis samples and 12 clusters in nonsepsis samples. 207 biomarker genes were extracted from the first 6 clusters by SVM method, and 10 genes including GCA were considered as potential biomarkers.

Computational and Mathematical Methods in Medicine
Tang et al. [38] explored the relationship between septic shock and AKI by analyzing codifferentially expressed genes (co-DEGs) in the hope of identifying possible genetic markers for septic shock-associated AKI. They downloaded two gene expression datasets (GSE30718 and GSE57065). DEGs related to septic shock and AKI were searched to clarify the molecular mechanism of DEGs through function analysis (GO), pathway enrichment analysis (KEGG), and protein interaction (PPI) network analysis. They also assessed co-DEGs and corresponding predictive miRNAs associated with septic shock and AKI. 16 genes, including NDUFAF1, were found to be involved in septic shockassociated AKI. Our study also found that NDUFAF1 expression was upregulated in patients with sepsis.
UBE2A, also called HHR6A or UBC2, can be expressed in a variety of tissues. Current studies mainly focus on cognitive impairment and skeletal muscle metabolism, but we have not found reports that UBE2A is directly related to sepsis. UBE2A may be associated with increased skeletal muscle protein catabolic activity in a number of diseases and malnutrition states, such as cancer, sepsis, and diabetes. Sepsis is often accompanied by septic encephalopathy, which is mainly manifested by changes in cognitive function and state of consciousness. It is necessary to further study whether UBE2A expression is abnormal in patients with septic encephalopathy [39,40].

Computational and Mathematical Methods in Medicine
In our study, there were 9 major differential genes involved in the development of sepsis. Five of these genes have been reported, indicating that the biomarkers selected by our random forest model have high diagnostic value. CLIC1, UFD1, SEPT9, and UBE2A are new biomarkers found by us through the random forest model, and there is no research report related to sepsis so far. These four genes may serve as relevant targets for the diagnosis and treatment of sepsis. Future in vitro and in vivo studies are needed to analyze the functions and pathways of these genes in the pathophysiology of sepsis. Further studies in more sepsis patients are needed to confirm the diagnostic value of the selected genes.

Conclusions
In this study, bioinformatics methods were used to analyze two septic shock-related datasets (GSE154918 and GES131761) and identify differentially expressed genes (DEGs) from GEO. We found that the number of differentially expressed genes increased with the increase of sepsis severity. It indicates that there are more genetic disorders from sepsis to septic shock. GO gene enrichment analysis showed that differential gene expression was significantly enriched in neutrophil activation and degranulation pathways. RAGE pathway has been found to be closely related to the occurrence of sepsis. Nine genes, including MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A, were identified to be associated with sepsis. Further studies of the role of these pathways and genes in sepsis patients or experiments are needed.

Data Availability
The data used to support the findings of this study have been deposited in the GEO database repository (GSE154918 and GES131761).

Conflicts of Interest
The authors declare that they have no competing interests.

Authors' Contributions
Jing Zhou, Siqing Dong, and Ping Wang contribute equally to this work whether that is in the conception, study design, execution, or data analyzing. Jing Zhou, Siqing Dong, and Ping Wang contribute equally to this work.


Evaluation of the entomopathogenic nematode, Steinernema asiaticum against the diamondback moth, Plutella xylostella (Linnaeus) (Lepidoptera: Plutellidae) under screen house and field conditions

Background: The experiments were conducted at Research Farm, Chaudhary Charan Singh Haryana Agricultural University, Hisar, India. Infectivity and effectiveness of entomopathogenic nematode (EPN), Steinernema asiaticum infective juveniles (IJs) were evaluated against the diamondback moth (DBM), Plutella xylostella (Linnaeus) (Lepidoptera: Plutellidae) as foliar spray on cabbage plants in screen house with single (30,000 IJs per plant), two (15,000 + 15,000 IJs per plant) or three (10,000 + 10,000 + 10,000 IJs per plant) applications. In the field trial, S. asiaticum (25,000 or 50,000 IJs per plant) and Malathion 50 EC (0.05%) singly or in combination at half the concentrations were used. Results: In screen house trial, all the treatments resulted in significantly high larval mortality than untreated plants. Split concentration application (15,000 + 15,000 IJs per plant) gave better DBM larval mortality (48.33%) compared to single application (36.00% at 30,000 IJs per plant). Comparing the two and three split applications, 15,000 IJs was significantly better (23.33% mortality) than 10,000 IJs (13.33%). In the field trial, S. asiaticum alone at 50,000 IJs resulted in 28.8% insect mortality compared to 18.0% in Malathion at 0.05%. Best results (37.5% mortality) were obtained by using nematode IJs at 25,000 per plant in combination with half the recommended concentration of Malathion (0.025%) leading to synergistic effect. Conclusions: The study revealed that split application (15,000 + 15,000 IJs) of EPN S. asiaticum IJs proved better than single application of IJs at the same concentration (30,000 IJs) in the management of P. xylostella larvae in the screen house conditions. In the field experiment, the best effectiveness was obtained in combination of half the concentrations of EPN and insecticide ( S. asiaticum IJs @ 25,000 per plant + Malathion @ 0.025%).

Background
India is one of the main producers of cruciferous vegetables worldwide, ranking second in production of cabbage, Brassica oleracea var. capitata L. that belongs to family Brassicaceae. Of the total area of vegetable grown in India, 5% is occupied by cabbage (Anonymous 2016). The

Open Access
Egyptian Journal of Biological Pest Control *Correspondence: pandeyshalini80@gmail.com major constraint in the production of cruciferous vegetables is the ravages of lepidopteran insect pests of which Plutella xylostella (Linnaeus) (Fam.: Plutellidae), Spodoptera litura (Fabricius) (Fam.: Noctuidae), Helicoverpa armigera (Hubner) (Fam.: Noctuidae) and Pieris brassicae (Linnaeus) (Fam.: Pieridae) are economically important. Diamondback moth (DBM), P. xylostella is the most noxious pest of cabbage and cauliflower worldwide. Attack of DBM results into both yield loss and marketable value of the crop. If incidence of P. xylostella occurs at an early stage of crop, heavy yield loss may occur.
Management of DBM has become difficult since the pest has developed resistance to many organophosphates, carbamate and pyrethroid insecticides due to their overuse (Shelton et al. 1993). Alternatively, Bacillus thuringiensis (Bt) Berliner (Bacillales: Bacillaceae) has been used widely for the management of P. xylostella (Cherry et al. 2004), but extended use of Bt also has resulted in the emergence of resistant DBM populations in Asia and the Americas (Ferre and Van Rie 2002).
Entomopathogenic nematodes EPNs have become potential candidates for successful biological control agents of lepidopteran pests (Kaya et al. 2006). Their ability to attack a wide host range of insect hosts, active host search, killing the host within 48 h, ease of mass production and application, long-term efficacy, compatibility with agro-chemicals and environmental safety have prompted their commercial use in many countries. EPNs are commonly found in soils with high sand content. EPNs were isolated from the soil of Research Farm, Hisar, Haryana and three species of EPNs viz., Steinernema asiaticum, Steinernema sp. RB-5 and Heterorhabditis bacteriophora were found from the collection (Kumar et al. 2014). In our earlier studies, steinernematids species were recovered more compared to heterorhabditids species in Northern Indian (Hisar, Haryana). The Environment Protection Agency (EPA) in India, USA, Australia and many European countries has exempted EPNs from registration (Ehlers 2005). Current dilemma on DBM management firmly emphasizes the urgent need for integrated pest management approach, of which biological control is a major component. In this study, the susceptibility of P. xylostella to EPNs in screen house and field conditions was investigated and also their compatibility with recommended insecticide (Malathion) on cabbage crop.

Mass rearing of insects
The stock culture of Greater wax moth, Galleria melonella (Linnaeus) (Lepidoptera: Pyralidae) was maintained in laboratory on artificial diet (corn flour 200 g, wheat flour 100 g, wheat bran 100 g, milk powder 100 g, yeast 50 g, glycerine 100 ml and honey 100 ml). Late instar larvae were used for multiplication of EPNs. Stock culture of P. xylostella was maintained on cabbage leaves in the laboratory (Dwivedi and Mathur 2000). Third instar larvae were used for experimental purpose.

Mass rearing of EPNs
Three species of EPNs (Steinernema asiaticum (Weiser) (Rhabditida: Steinernematidae), Steinernema sp. RB-5 and Heterorhabditis bacteriophora (Poinar) (Rhabditida: Heterorhabditidae) were originally isolated from the soil of Research Farm, Hisar, Haryana. All the EPN isolates were found pathogenic to P. xylostella larvae in preliminary laboratory assays causing a significant mortality. S. asiaticum was adjudged the most pathogenic among the test nematodes on the basis of highest mean mortality (36.71%) and highest mortality (65.62%) after 96 h (Kumar et al. 2014). Indigenous strains of EPNs were reared in vivo on G. mellonella in laboratory. IJs were extracted from cadavers by 'White Trap' method (White 1927) and used for the experimental purpose.

Standardization of EPN formulation
To standardize the formulation, the IJs were applied in 50 ml of sterile water containing glycerine (1%) as antidessicant, Triton x-100 (1%) as spreader, PABA (Para Amino Benzoic Acid) (0.05%) and Congo red (0.05%) as UV protectants as adopted by Walia et al. (2008). While testing the efficacy of EPNs against larvae of diamondback moth, P. xylostella, it was noticed that PABA caused 60-70% EPN mortality, whereas Congo red proved safe to EPN. Therefore, PABA was replaced with Congo red in the final formulation. Congo red has been suspected to be considered as health hazardous, but at this low concentration it has recommended as UV Protectants (Hadapad et al. 2009). Moreover, Congo red is a sodium salt with negligible evaporation rate in the environment, stable at room temperature, oral LC 50 -15,200 mg/kg, and teratogenicity occurs at 200 mg/kg/day and above. Hazardous class 4.1. No component in this product was listed as hazardous substance under clean water act and clean air act (Air Pollutants) according to US Federal Agency. Congo red is amenable to photo-degradable and not absorbed by plant and also not volatilized in the air. It is also used in tracing of certain human diseases pertaining to kidney, liver, brain, etc. (Yakupova et al. 2019). Even, if cabbage is processed and cooked, before processing upper two leaves were removed which are exposed to Congo red.

Green house evaluation
Earthen pots of (30 × 20 cm diameter) filled with field soil were transplanted (15-25 December) with cabbage Page 3 of 8 Kumar et al. Egyptian Journal of Biological Pest Control (2022) 32:90 (cv. Golden acre) seedlings (@ 2 per pot). One month after transplanting, 10 individuals of 3rd instar larvae of P. xylostella were placed in the whorl of each plant. Different levels (30,000 IJs in single spray, 15,000 IJs in 2 sprays at 4-day interval and 10,000 IJs in 3 sprays at 4-day interval) per plant were sprayed in 50 ml of sterile water containing glycerine (1%), Triton x-100 (1%) and Congo red (0.05%). Hand atomizer was used to spray the mixture. After spraying, the plants were covered with paper cage so that larvae do not escape from the pots. Each treatment was replicated five times. In control, the plants were sprayed with 50 ml of water along with above-mentioned additives. Observations were recorded on dead larvae 24, 48 and 72 h after spraying. The dead larvae were taken to laboratory for estimating nematode population by the white trap method (White 1927). The data obtained were subjected to analysis of variance using the completely randomized design (CRD) (Snedecor and Cockran 1968).

Field evaluation
The experiment was conducted in a cabbage field at Research Farm, Chaudhary Charan Singh Haryana Agricultural University, Hisar, India, in the month of April. Cabbage plant infested with P. xylostella larvae was randomly selected for evaluation, and to maintain equal number of larvae on each infested plant excess larvae were removed with camel hair brush. The uniformity of DBM larvae (i.e. 10 larvae/plant) was maintained by removing excess larvae from the plant. The movement of the larvae among the plants is very slow. There was no further scope of invasion of larvae during the experimental period (48 h). It was evident from the control plants where the larva was same after 48 h. Different treatments (T1 = 50,000 IJs with glycerine (1%) and Triton x-100 (1%), T2 = Malathion 50 EC @ 0.05%, T3 = 25,000 IJs with glycerine (1%) and Triton x-100 (1%) + Malathion 50 EC @ 0.025%, T4 = 25,000 IJs with glycerine (1%) and Triton x-100 (1%), T5 = control; water with glycerine (1%) and Triton x-100 (1%) per plant were applied in 50 ml of distilled water in the evening time. Hand sprayer was used to spray the mixture. Each treatment was replicated four times. Observations were recorded on dead larvae 24 and 48 h after spraying. To check the efficacy of EPNs against the larvae could have been correlated with the leaf damage.
But, it was difficult to correlate the leaf damage with the population of this insect because larvae are very slow feeder and have slow growth. The data obtained were subjected to analysis of variance using completely randomized design (CRD) (Snedecor and Cockran 1968).

Green house test
The bubble formation on the body of EPN-infected DBM larvae indicated early symptoms of infectivity. In the present study, dead larvae of P. xylostella turned to greyish/black due to infection of EPN, S. asiaticum. The fluid-filled bubbles harboured actively moving adults or infective juveniles (IJs) of EPNs. It is speculated that the gaseous emission resulting from the decomposing insect body by the bacterial symbionts was trapped in the thin membranous tissues. These bubbles finally burst and release the IJs (Fig. 1). However, Jang et al. (2012) reported the bubble formations due to catalase produced by the bacteria Photorhabdus temperata M1021 isolated from the EPNs. The highest mortality (36%) of DBM larvae due to EPN after first spray was recorded at 30,000 IJs concentration, and it was significantly higher than the 15,000 and 10,000 IJs concentrations (Table 1). All the 3 concentrations of EPNs resulted in significantly higher mortality than adjuvants and water alone. The effect of time and its interaction with concentration was non-significant (Table 1). However, the data on the effect of second foliar spray in the case of split application of S. asiaticum once again revealed significantly higher larval mortality than the controls. Among the two concentrations, 15,000 IJs was significantly better mortality (23.33%) than 10,000 IJs (13.33%). The effect of time factor and its interaction with concentration was non-significant (Table 2). Pooled data of the first and second sprays revealed that split application (15,000 + 15,000 IJs) of S. asiaticum IJs proved better than single application of 30,000 IJs and resulted in 48.33% mortality ( Table 3). The third treatment was originally intended for three sprays (10,000 + 10,000 + 10,000 IJs); however, all the left-over larvae turned into pupae by the time of application of third spray. So, the third spray could not be undertaken. The effect of time was also significant with a high larval mortality at each time interval. The interaction of time and concentration was, however, non-significant. Adults of the EPN (S. asiaticum) wriggling on the dead larvae of DBM (Fig. 2) were very prominent. The recovery of IJs from dead cadavers of P. xylostella after the first/second spray in different treatments ranged (95-439), and the differences were significant (Fig. 3).

Field evaluation
Maximum DBM larval mortality (37.5%) was recorded in treatment T3 (25,000 IJs + Malathion 50 EC 0.025%) which was significantly better than all other treatments ( Table 4). As compared to control, minimum insect mortality (11.3%) was recorded in T4 (25,000 IJs), followed by 18.0% in T2 (Malathion 50 EC 0.05%) and 28.8% in T1 (50,000 IJs). All the treatments resulted in significantly higher larval mortality compared to control. The effect of time and its interaction with treatments was non-significant.

Discussion
Entomopathogenic nematodes (EPNs) have been widely utilized to manage wide range of insect pests both below and above ground (Bhairavi et al. 2021). They can be used solely as a biological control agents or combined with other management methods in integrated pest management (Kumar et al. 2013 Koppenhöfer et al. (2020a) reported that splitting the concentration of S. carpocapsae (2.5 × 10 9 IJs ha −1 ) into two applications provided significantly highest control of Listronotus maculicollis (Kirby) (Coleoptera: Curculionidae) larvae than in one application. The combination of imidacloprid with the split S. carpocapsae application also provided significantly higher control (88-95%) than the respective single-agent treatment. In another findings, S. carpocapsae efficacy was improved by 13% through splitting into two applications at half rate against larvae of the black cutworm, Agrotis ipsilon (Hufnagel) (Lepidoptera: Noctuidae) (Koppenhöfer et al.2022). The logic of using split application in screen house experiment was to enhance the chances of contact between IJs and P. xylostella larvae. But, the duration of 3rd instar larvae of DBM was very short (3-4 days) because of which the full potential of EPNs in the containment of P. xylostella was not fully realized. Therefore, splitting EPN into two applications at half rate could be a potential method for improving EPN performance as recorded in present studies. This is because after inundative applications of billions of IJs per ha −1 populations tend to decline pest population quickly as noticed in earlier studies (Ebssa and Koppenhöfer 2011). Other Page 5 of 8 Kumar et al. Egyptian Journal of Biological Pest Control (2022)    Page 6 of 8 Kumar et al. Egyptian Journal of Biological Pest Control (2022) 32:90 workers have also reported the efficacy of different Steinernema spp. against DBM (Belair et al. 2003). Kaya and Gaugler (1993) documented that the field effectiveness of EPNs was very less than the laboratory tests. Soil moisture and temperature may affect the efficiency of those EPNs as they need some moisture for survival. DBM larvae cease feeding and may close their spiracles when sprayed by excessive amounts of water (Baur et al. 1995). This might explain the reduced percentage mortality in DBM larvae under field conditions when plants were drenched with the spray suspension, as compared to laboratory tests where the appropriate moisture percentage is maintained. UV light, warmer temperatures, dessication and the features of exposed foliage can promote lesser EPN activation in greenhouse circumstances than the laboratory conditions (Vashisth et al. 2013). Schroer et al. (2005a) reported that the same formulation decreased mobility of DBM larvae and at the same time provided conditions which enhanced nematode host seeking and invasion of the target insect. Waxy substance in the upper surface of leaves in cabbage perhaps leads to adherence of spray fluid containing nematodes. Spray fluid glued in form of droplet and move downward from leaf surface. Larvae feeding on the upper surface of leaves were attacked easily by the EPNs rather than larvae feeding on lower surface. DBM larvae preferably feed on lower surface of leaves (Saravanapriya and Subramanian 2006). Baur et al. (1995) also reported that P. xylostella mortality  Page 7 of 8 Kumar et al. Egyptian Journal of Biological Pest Control (2022) 32:90 was higher in pubescent leaves than in glaborous leaves (cabbage plants). Susceptibility to nematodes was greater for early versus late 3 rd instar DBM larvae (Baur et al. 1998). The factors outlined might have influenced present studies also, thereby leading to variation of results of efficacy in screen house and field studies. Foliar application of EPNs exposes the IJs to the vagaries of dessication and UV radiation. The use of anti-desiccants and UV protectants along with surfactants into the nematode suspension was therefore necessitated (Schroer and Ehlers 2005). Schroer et al. (2005b) screened several adjuvants for toxicity on nematodes, plants or insects and tested different combinations of surfactants and polymers for their potential to improve nematode efficacy. Therefore, in present experiments, the concentrations of the adjuvants (glycerine, Triton X-100) were first standardized in the laboratory assays against S. asiaticum and only sub-lethal concentrations were used in this study. The nematodes were sprayed during the evening time between 5:30 to 6:30 PM to minimize UV and immediate desiccation and hence obtained good mortality in DBM larvae. Low humidity is a major limiting factor for nematode survival on foliage (Glazer 2002). Glazer (1992) recorded a drastic reduction in nematode efficacy 6 h after application to foliage. It was observed cessation in IJ movement after application to plant foliage, suggesting that further EPN uptake happened by insects consuming the IJs. There are other ways to optimize the efficacy of EPNs for greenhouse and field applications like preparing EPNs formulation, and EPNs can be combined with other control agents like chemical insecticides and other biological control agents like fungus and bacteria (Koppenhöfer et al. 2020b) as tested in the present studies. It appeared that reduced concentrations of Malathion were less detrimental to EPNs and at the same time reduced the activity and feeding and growth activities of DBM larvae leading to enhanced mortality.

Conclusions
The efficacy of EPN, S. asiaticum against the DBM, P. xylostella larvae was tested under screen house and field conditions. Split applications (15,000 + 15,000 IJs per plant) of S. asiaticum were more effective than single application (30,000 IJs per plant) under screen house conditions. In field conditions, combinations of half the concentration of EPNs as well as insecticide (S. asiaticum @ 25,000 per plant + Malathion 50 EC @ 0.025%) proved more effectiveness than single treatment (S. asiaticum @ 50,000 per plant or Malathion 50 EC @ 0.05%). Splitting EPNs into two applications at half rate could be the potential method for improving efficacy of EPNs in the management of DBM, and also combination of half the concentration of Malathion with the split concentration of S. asiaticum provided significantly higher control than the respective singleagent treatment. The most effective, long-term way to manage pests is by using a combination of available pest control methods that work better together than separately, by the most economical means and with the least possible hazard to consumer as well as environment.  Page 8 of 8 Kumar et al. Egyptian Journal of Biological Pest Control (2022) 32:90